The certificate program has tasked me with analyzing the data to come up with ideas for how to increase employee retention. To help with this, they would like me to design a model that predicts whether an employee will leave the company based on their department, number of projects, average monthly hours, and any other data points me deem helpful.
The steps I took were:
- Setup reproducible and versionable python environment, something I learned from my software engineering background
- Familiarize myself with the dataset
- Conduct Exploratory Data Analysis
- Gather descriptive statistics (mean, std, quantiles)
- Cleanup missing and duplicate data
- Check for outliers
- Further examine various relationship between features thru visualization
- Train 3 models using Logistic Regression, DecisionTree and RandomForest
- Compare the performance of the models
- Report my findings
see main.ipynb file.
sourced from https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction?select=HR_comma_sep.csv
Hr Data Analytics This dataset contains information about employees who worked in a fictitious company.
| Variable | Description |
|---|---|
| satisfaction_level | Employee-reported job satisfaction level [0–1] |
| last_evaluation | Score of employee's last performance review [0–1] |
| number_project | Number of projects employee contributes to |
| average_monthly_hours | Average number of hours employee worked per month |
| time_spend_company | How long the employee has been with the company (years) |
| Work_accident | Whether or not the employee experienced an accident while at work |
| left | Whether or not the employee left the company |
| promotion_last_5years | Whether or not the employee was promoted in the last 5 years |
| Department | The employee's department |
| salary | The employee's salary (U.S. dollars) |
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